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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr>\n",
      "# PYTHON FOR DATA SCIENCE\n",
      "\n",
      "- Part 1: Overview\n",
      "- Part 2: Case Study\n",
      "\n",
      "### An Overview of Scientific Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Contents\n",
      "\n",
      "- [IPython](#IPython)\n",
      "- [NumPy](#NumPy)\n",
      "- [SciPy](#SciPy)\n",
      "- [Matplotlib](#Matplotlib)\n",
      "- [pandas](#pandas)\n",
      "- [scikit-learn](#scikit-learn)\n",
      "- [statsmodels](#statsmodels)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"IPython\"></a>\n",
      "<hr>\n",
      "\n",
      "## IPython\n",
      "\n",
      "Command line and web IDE designed for use with Python and several other scientific and web-based technologies."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### What is it?\n",
      "\n",
      "### A computing architecture for Python composed of \n",
      "\n",
      " - 2 Python shells (terminal and Qt-based)\n",
      "\n",
      "- A web notebook that runs in the browser."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### What can you do with it?\n",
      "\n",
      "## Code completion\n",
      "\n",
      "#### ```IPython``` tells you about the objects you're working with, showing you helpful usage info about functions, methods available, datatypes, and more."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "things = [\"First Name\", \"Last Name\"]\n",
      "\n",
      "def snakify(txt):\n",
      "    \"return string in snake_case\"\n",
      "    return txt.replace(\" \",\"_\").lower()\n",
      "\n",
      "print [snakify(thing) for thing in things]\n",
      "\n",
      "\n",
      "snakify( \"My Name is Greg\" )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['first_name', 'last_name']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'my_name_is_greg'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<bold class=\"bold\">Tip: </bold>Comment and uncomment lines with ``` command + / ``` if you're on a mac."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Interact with the command-line\n",
      "\n",
      "#### Use many shell commands directly from IPython, including shell scripts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# try these out too\n",
      "! pwd\n",
      "!ls -lt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/Users/glamp/repos/yhat/presentations/DataGotham2013/notebooks\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 3008\r\n",
        "-rw-r--r--   1 glamp  staff  324153 Sep 12 10:41 1 - Tools Overview.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff  390643 Sep 12 10:32 salary_data.csv\r\n",
        "-rw-r--r--   1 glamp  staff   11176 Sep 12 09:44 2 - Case Study.ipynb\r\n",
        "drwxr-xr-x  11 glamp  staff     374 Sep 12 09:08 \u001b[36mdata\u001b[m\u001b[m\r\n",
        "-rw-r--r--   1 glamp  staff    7824 Sep 12 09:06 9 - Deployment.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff  130269 Sep 12 08:38 8 - Fitting and Evaluating Your Model.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff  232588 Sep 12 08:35 7 - Feature Engineering.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff   97159 Sep 12 08:33 6 - Aggregation and Grouping.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff   14396 Sep 12 08:31 5 - Imputing Data.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff  127362 Sep 12 08:29 4 - scikit-learn basics.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff  102884 Sep 12 08:28 3 - Importing Data.ipynb\r\n",
        "-rw-r--r--   1 glamp  staff   54409 Sep 11 21:45 iris.png\r\n",
        "-rw-r--r--   1 glamp  staff     978 Sep 11 21:45 iris.dot\r\n",
        "-rw-r--r--   1 glamp  staff   13186 Sep 11 12:00 iris.pdf\r\n",
        "-rw-r--r--   1 glamp  staff    2903 Sep  9 17:19 outline.txt\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Capture the output of a shell command and store the results in a python variable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "foo = ! echo hello, world\n",
      "foo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "['hello, world']"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Use curl to download data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# download salary_data.csv save contents to \n",
      "# a local file in the same directory as this notebook\n",
      "!curl http://www.justinmrao.com/salary_data.csv >> ./salary_data.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
        "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
        "\r",
        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 28  381k   28  108k    0     0   159k      0  0:00:02 --:--:--  0:00:02  365k"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100  381k  100  381k    0     0   462k      0 --:--:-- --:--:-- --:--:--  863k\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# peak at the first row\n",
      "! head -n 1 salary_data.csv | tr -s \",\" \"\\n\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "team\r\n",
        "year\r\n",
        "player\r\n",
        "contract_years_remaining\r\n",
        "contract_thru\r\n",
        "position\r\n",
        "full_name\r\n",
        "salary_year\r\n",
        "salary_total\r\n",
        "year_counter\r\n",
        "obs\r\n",
        "mean_salary\r\n",
        "mean_remaining\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# peak at the second row\n",
      "! head -n+2 salary_data.csv | tail -n-1 | tr -s \",\" \"\\n\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"Boston Celtics\"\r\n",
        "\"2002-03\"\r\n",
        "\"Bremer\r\n",
        " J.R.\"\r\n",
        "1\r\n",
        "\"2002-03\"\r\n",
        "\"G\"\r\n",
        "\" Bremer\"\r\n",
        "349458\r\n",
        "349458\r\n",
        "1\r\n",
        "2\r\n",
        "456568.5\r\n",
        "1\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Include rich media and javascript\n",
      "#### IPython gives you inline plots and allows you to use virtually any browser supported media."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Inline plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "X = range(10)\n",
      "y = range(11,21)\n",
      "pl.scatter(X,y, c='r')\n",
      "pl.show()\n",
      "print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jB4AAoSMHgBBikRtyvacjn91czudyNlMscgCwHB05AAQIHTkAhBCL3JDrPR357OZyPpez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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105b09050>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Write and run javascript among other languages including Ruby, Octave, Julia, R, etc."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%javascript\n",
      "\n",
      "function say_hi ( ) {\n",
      "    alert(\"Hello, World\");\n",
      "};\n",
      "\n",
      "say_hi();\n",
      "console.log(\"Welcome!\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "\n",
        "function say_hi ( ) {\n",
        "    alert(\"Hello, World\");\n",
        "};\n",
        "\n",
        "say_hi();\n",
        "console.log(\"Welcome!\");"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x10602bdd0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Render images from the web via a url or images on your local computer via the filename."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(\"http://ipython.org/_static/IPy_header.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.Image at 0x10602bcd0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Render video"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.lib.display import YouTubeVideo\n",
      "YouTubeVideo('SncapPrTusA', width=680, height=400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"680\"\n",
        "            height=400\"\n",
        "            src=\"http://www.youtube.com/embed/SncapPrTusA\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x10602b8d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"NumPy\"></a>\n",
      "<hr>\n",
      "\n",
      "## NumPy\n",
      "\n",
      "One of the fundamental packages for scientific computing with Python. Many other popular packages and projects use NumPy under the hood, so it's pretty helpful to be familiar with its core concepts."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### What is it?\n",
      "\n",
      "### A collection of tools, utilities, and data structures for working with data.\n",
      "\n",
      "- A powerful N-dimensional array object\n",
      "- Element by element broadcasting operations (as opposed to iterating)\n",
      "- Tools for integrating C/C++ and Fortran code\n",
      "- Linear algebra and other mathematical and random number facilities\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## N-dimensional arrays\n",
      "\n",
      "#### The ```ndarray``` is a bit like a python list only more efficient."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"example taken from Scipy and NumPy by Eli Bressert (p. 6)\"\"\"\n",
      "import numpy as np\n",
      "\n",
      "def list_times(alist, scalar):\n",
      "    for i, val in enumerate(alist):\n",
      "        alist[i] = val * scalar\n",
      "    return alist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr = np.arange(1e7)\n",
      "l   = arr.tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit arr * 1.1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 35.4 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit list_times(l, 1.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 1.59 s per loop\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### ```ndarray```s behave somewhat like lists."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"len(l)\", len(l)\n",
      "print \"len(arr)\", len(arr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "len(l) 10000000\n",
        "len(arr) 10000000\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### The ```ndarray``` is capible of more advanced slicing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = [ [1,2], [3,4] ]\n",
      "arr   = np.array(l)\n",
      "\n",
      "print \"Value in Row One, Column One: %d\" % l[0][0]\n",
      "print \"Value in Row One, Column One: %d\" % arr[0,0]\n",
      "print \"Value in All Rows, Column Two: %s\" % str(arr[::,1])\n",
      "print \"Value in Row Two, Both Columns: %s\" % str(arr[1::,])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Value in Row One, Column One: 1\n",
        "Value in Row One, Column One: 1\n",
        "Value in All Rows, Column Two: [2 4]\n",
        "Value in Row Two, Both Columns: [[3 4]]\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### You can access elements stored in an ```ndarray``` using ```arr[]``` sub notation ```arr[rows, columns]```.\n",
      "\n",
      "#### Select the entire 10th row like so ```arr[9, :]```.  ```9``` indicates \"row 10\" and ```:``` indicates <em>all</em> columns."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zero_to_1000 = np.arange(0,1000)               # create an array of integers from 0 to 1000\n",
      "zero_to_1000 = zero_to_1000.reshape( (500,2) ) # reshape into 2 dimensions (500 Rows x 2 Cols2)\n",
      "zero_to_1000[:100, 1]                          # select the 2nd columns from the top 100 rows"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "array([  1,   3,   5,   7,   9,  11,  13,  15,  17,  19,  21,  23,  25,\n",
        "        27,  29,  31,  33,  35,  37,  39,  41,  43,  45,  47,  49,  51,\n",
        "        53,  55,  57,  59,  61,  63,  65,  67,  69,  71,  73,  75,  77,\n",
        "        79,  81,  83,  85,  87,  89,  91,  93,  95,  97,  99, 101, 103,\n",
        "       105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129,\n",
        "       131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155,\n",
        "       157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181,\n",
        "       183, 185, 187, 189, 191, 193, 195, 197, 199])"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### NumPy arrays support boolean indexing for selection."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numbers = np.random.uniform(size=100)\n",
      "numbers = numbers.reshape((50,2))\n",
      "mask = (numbers >= 0.7) & (numbers <= 0.9)\n",
      "mask[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([[False,  True],\n",
        "       [False, False],\n",
        "       [ True, False],\n",
        "       [False, False],\n",
        "       [ True, False],\n",
        "       [False, False],\n",
        "       [False, False],\n",
        "       [False, False],\n",
        "       [False,  True],\n",
        "       [False, False]], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numbers[mask, :] # pass the boolean mask to the array using sub notation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([ 0.86092406,  0.82335412,  0.77923436,  0.75099096,  0.846525  ,\n",
        "        0.78825028,  0.8995129 ,  0.87703628,  0.81306635,  0.82112517,\n",
        "        0.83669703,  0.89527147,  0.7700211 ,  0.74010591,  0.81181264,\n",
        "        0.74089343,  0.80139149,  0.78089882])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"SciPy\"></a>\n",
      "<hr>\n",
      "\n",
      "## SciPy\n",
      "\n",
      "SciPy contains functions and utilities for common scientific tasks. Most of SciPy's features rely on NumPy and many other scientific projects rely on SciPy.\n",
      "\n",
      "\n",
      "We aren't using SciPy directly in this tutorial, and since it's rather large in scope, we'll just give you some of the project highlights.\n",
      "\n",
      "- Optimization and Minimization\n",
      "    - 80 continuous distributions\n",
      "    - 60 statistical functions\n",
      "- Interpolation\n",
      "- Integration\n",
      "- Statistics\n",
      "- Spatial and Clustering Analysis\n",
      "- Signal and Image Processing\n",
      "- Sparse Matrices"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"Matplotlib\"></a>\n",
      "<hr>\n",
      "\n",
      "## Matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make some data to plot\n",
      "x = np.linspace(start=0, stop=2*np.pi, num=50)\n",
      "y1 = np.sin(x)\n",
      "y2 = np.cos(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Lines"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1, figsize=(12,6))\n",
      "ax.plot(x, y1, label='sin')\n",
      "ax.plot(x, y2, label='cos')\n",
      "\n",
      "ax.legend(fontsize=16)\n",
      "ax.axes.xaxis.set_label_text(\"X Axis\", fontdict={\"size\":22})\n",
      "ax.axes.yaxis.set_label_text(\"y Axis\", fontdict={\"size\":22})\n",
      "ax.title.set_text(\"This is a graph\\n\")\n",
      "ax.title.set_fontsize(28)\n",
      "fig.show()\n",
      "print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/figure.py:361: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
        "  \"matplotlib is currently using a non-GUI backend, \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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q1B56UifpZmYuwqx7Y2ArtcQv359Bh1K4a5U3dnLUjBsIjru2LOL9Me54cVc+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\nQoWkRSGoL1Ncszc5MR6xrX+ZTIanE7zw7XktGroGWMe5KWPXvlHbiQt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LwsLlkbCyMTdy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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106033a90>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Histograms"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = randn(100) * 100.\n",
      "m = d.mean()\n",
      "s = d.std()\n",
      "m_y = 1.5\n",
      "\n",
      "fig = figure(figsize=(12,6))\n",
      "ax = subplot(111)\n",
      "ax.hist(d, 15)\n",
      "ax.plot(m, m_y, \"ko\")\n",
      "ax.plot([m - s, m + s], [m_y] * 2, \"k--\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1060cf190>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Multiple plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(0, 100)\n",
      "y = np.random.rand(100)  # 100 random numbers\n",
      "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2,figsize=(10,10))\n",
      "ax1.plot(x, y)\n",
      "ax2.hist(y)\n",
      "ax3.scatter(x, y)\n",
      "ax4.boxplot(y)\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A5PMhPjSLHrR9ecAok4FTMRSmkfCAtT2CLXWnvxgKwhkwoQDrsYYKGVkm/EFn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\nRKQxFmBEREREGmMBRkRERKSx/we/qB5OR0dhYAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d7bfc50>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## ggplot2 matplotlib theme\n",
      "\n",
      "- [https://s3.amazonaws.com/demo-datasets/matplotlib.zip](https://s3.amazonaws.com/demo-datasets/matplotlib.zip)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#if you already have a matplotlib config file, you'll need to run this in the terminal\n",
      "! unzip ~/Downloads/matplotlib.zip -d ~/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Archive:  /Users/glamp/Downloads/matplotlib.zip\r\n",
        "   creating: /Users/glamp/.matplotlib/\r\n",
        "  inflating: /Users/glamp/.matplotlib/.DS_Store  \r\n",
        "   creating: /Users/glamp/__MACOSX/.matplotlib/\r\n",
        "  inflating: /Users/glamp/__MACOSX/.matplotlib/._.DS_Store  \r\n",
        "  inflating: /Users/glamp/.matplotlib/fontList.cache  \r\n",
        "  inflating: /Users/glamp/.matplotlib/matplotlibrc  \r\n",
        "   creating: /Users/glamp/.matplotlib/tex.cache/\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Matplotlib takes some getting used to, but don't fret. There are some solid examples and recipes available online that are super helpful.\n",
      "\n",
      "[http://matplotlib.org/gallery.html](http://matplotlib.org/gallery.html.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"pandas\"></a>\n",
      "<hr>\n",
      "\n",
      "## pandas\n",
      "\n",
      "#### pandas is the utility belt for data analysts using python. The package centers around the pandas ```DataFrame```, a two-dimensional data structure with indexable rows and columns. It has effectively taken the best parts of Base R, R packages like plyr and reshape2 and consolidated them into a single library.\n",
      "\n",
      "#### Library highlights include:\n",
      "\n",
      "- DataFrame\n",
      "- Indexable rows and columns (e.g. selecting columns and rows via column/row names)\n",
      "- Boolean indexing\n",
      "- Graceful treatment of missing or ```NA``` values\n",
      "- Broadcasting (i.e. calling a function on an entire row or column without iteration)\n",
      "- Outstanding ```split```, ```apply```, ```combine```, ```groupby``` functionality\n",
      "- Simple yet powerful string manipulation (again, applied over a large number of strings)\n",
      "- Built-in summary statistics (```mean```, ```std```, ```corr```, etc.)\n",
      "- Merging, joining, data set concatenation\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "df = pd.DataFrame({\"A\": range(10), \"B\": np.random.random(size=10)})\n",
      "df.B.corr(df.A)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "0.29593870451714266"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"scikit-learn\"></a>\n",
      "<hr>\n",
      "\n",
      "## scikit-learn\n",
      "\n",
      "scikit-learn is composed of a wide number of machine learning algorithms, utilities, and transformations all following a common and consistent API.\n",
      "\n",
      "#### Library highlights include:\n",
      "\n",
      "- Biclustering\n",
      "- Clustering\n",
      "- Covariance estimation\n",
      "- Cross decomposition\n",
      "- Dataset examples\n",
      "- Decomposition\n",
      "- Ensemble methods\n",
      "- Gaussian Process for Machine Learning\n",
      "- Generalized Linear Models\n",
      "- Manifold learning\n",
      "- Gaussian Mixture Models\n",
      "- Nearest Neighbors\n",
      "- Semi Supervised Classification\n",
      "- Support Vector Machines\n",
      "- Decision Trees"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load http://scikit-learn.org/stable/_downloads/plot_lena_ward_segmentation.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "===============================================================\n",
      "A demo of structured Ward hierarchical clustering on Lena image\n",
      "===============================================================\n",
      "\n",
      "Compute the segmentation of a 2D image with Ward hierarchical\n",
      "clustering. The clustering is spatially constrained in order\n",
      "for each segmented region to be in one piece.\n",
      "\"\"\"\n",
      "\n",
      "# Author : Vincent Michel, 2010\n",
      "#          Alexandre Gramfort, 2011\n",
      "# License: BSD 3 clause\n",
      "\n",
      "print(__doc__)\n",
      "\n",
      "import time as time\n",
      "import numpy as np\n",
      "import scipy as sp\n",
      "import pylab as pl\n",
      "from sklearn.feature_extraction.image import grid_to_graph\n",
      "from sklearn.cluster import Ward\n",
      "\n",
      "###############################################################################\n",
      "# Generate data\n",
      "lena = sp.misc.lena()\n",
      "# Downsample the image by a factor of 4\n",
      "lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]\n",
      "X = np.reshape(lena, (-1, 1))\n",
      "\n",
      "###############################################################################\n",
      "# Define the structure A of the data. Pixels connected to their neighbors.\n",
      "connectivity = grid_to_graph(*lena.shape)\n",
      "\n",
      "###############################################################################\n",
      "# Compute clustering\n",
      "print(\"Compute structured hierarchical clustering...\")\n",
      "st = time.time()\n",
      "n_clusters = 15  # number of regions\n",
      "ward = Ward(n_clusters=n_clusters, connectivity=connectivity).fit(X)\n",
      "label = np.reshape(ward.labels_, lena.shape)\n",
      "print(\"Elapsed time: \", time.time() - st)\n",
      "print(\"Number of pixels: \", label.size)\n",
      "print(\"Number of clusters: \", np.unique(label).size)\n",
      "\n",
      "###############################################################################\n",
      "# Plot the results on an image\n",
      "pl.figure(figsize=(5, 5))\n",
      "pl.imshow(lena, cmap=pl.cm.gray)\n",
      "for l in range(n_clusters):\n",
      "    pl.contour(label == l, contours=1,\n",
      "               colors=[pl.cm.spectral(l / float(n_clusters)), ])\n",
      "pl.xticks(())\n",
      "pl.yticks(())\n",
      "pl.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "===============================================================\n",
        "A demo of structured Ward hierarchical clustering on Lena image\n",
        "===============================================================\n",
        "\n",
        "Compute the segmentation of a 2D image with Ward hierarchical\n",
        "clustering. The clustering is spatially constrained in order\n",
        "for each segmented region to be in one piece.\n",
        "\n",
        "Compute structured hierarchical clustering..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Elapsed time: ', 11.367425918579102)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Number of pixels: ', 65536)\n",
        "('Number of clusters: ', 15)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3TNkrLNNQif+T6TbU0Un2TZilRZO+vQzhVVtfiu4iOb87dp0Xrdk8h/Wcaix8\nAq6KpLRo+KE/lt96PrnySZdfgSWor0lF/UkW7412Aoef/hp0WlUXavP1Q+12OxM6drtd17euINLY\nss8oBmbOvRTty3+B9uU/R/HYQSACnv6Jz+PkC0/Hwfl5Z8zsNgfl3UrOyyq+/cznsGjIlJ92HlUu\n1SlYetgU+di5VpnRHz0K1sptLwTnK5ex/am8RVGEmZkZ9w44+6IBPjufz7t9cQcccADK117rDNKH\nSyVcMTiYQVfAUi2SyqIP1ep4aQT1kEVfbrRX26cNuTqpJFbrkCjUPB+YKImowVpKFRBf+GXpVmPk\nE2plii1sU0OgDMsFedxyROrgH3/5xDIvpwxUwVDhUu+nhog0ruZ9rcAUCgX09fWhr68PlUoFlUrF\nLffq2GgYeEA+6aKX5FJwsVhEkiS4b93hCPoBJEB3ex2PefcbEXfrjv52u+2Qlb7tQ/MTqiwqbOkc\n59FMBhDlx9D47KUoHjsEJEC8o4unfekcPKTUcPk1WyOkvGW/nEv9TA2JKr1vRUtlpdccqFGyBkrl\nyhoOnwFk07m3MmGNhL1XS0OsnFgZ9LVut4vJyUlMTU2h0WhkdIT3a9J7eGEBL7k0XX39cKmE7f39\ny0JQ0qV6asEAP1djRjRladiX0A3Yx/OUNNvOuFrzSO12G81mEwsLC2g0Gmg2mxlraVGQHaQtVlPe\n+6wrn635A/UgGpdbz6WMv+iIk5AAGJpbXiRoBRBYOoNajY7Sr3zjb0u/hfAscCuVShgaGsLatWsx\nMjKC/kUhaTabqNVqbhmecJ2rXszj0ajo+Nzf+SIu++QXcf+7X4RgTYBkBjjyw29yc0Ne6VwSgttD\n0+ho3GuZk+wKWrfbRfVTv0FwwakoPn8tkAB/++kzcNL15+KwZLdDvOyLdKtMKY+sA/J9rnKhDoiG\n2xouRTwWGfiep8ZA576X1/chOnsGtw1/9H9boEta1Lj5IhCeIjA/P58BB0p/GIbo6+tDfMstAIA7\nAVxaqWTosLpu+UZ6LM9V1y060sWx1do+HQVnwzbrEWiQ8NDLseZZP0K3tHROr06gWlU7MVkUtTTZ\nirI076MePkmSTJ2Ghm9kHBkDyDEhyIZcNifhg84qGHZpVO/3TaDNe+TzefeW0zVr1qBSqaDVaqFe\nr2N+ft5V65J2Gg7lbRRFrnao1WohSdJtCPV6PVvvVCzj7gcdi8s/9uWUpp3A1jvPwyG//xzy3aor\nV2g2mwAUNGJpAAAgAElEQVSAcrns5kjPyiFqIyKL4zjzTjYgVdz61mei9qFfovTKbelYL5nAsV/7\nIY4MHnC8YAjZarXcSwiUf2p47AKKCr/Kj/Jdr7HGzSa6LVryGSQ7/xqOkTfWcKqjUMPI5tMBfs77\nbDiu49fvoyjC9PQ0JiYmMD8/nzHIvJY60ppO84wRsi/zsA5Vy0zUCLN/zh0BCiv11TGSP0r3Sm3F\n1Tc1BKwR0txAs9lEo9FArVbDuud/B09/3ERKyLHn48efeQvCcMyb5NL8SBz73yLCRtQDLCEVTrp6\neDup9rlWiG3is9TZiXZxc0bggKXqXV7b7XZRKpUyk90rFPDlIIIgwPDiwVi1Wg0TExOYnJx0b25V\n1KD5GzsnihLDMHQCoQoYhqHLDVFw//YjbwHVduiD/w0AOLTvGtz6zbMRIw0f6a0LhQJqtRoGBgYw\nMDCQ2RRNdNbX14dcLueKM/k/Fbb+7h+icuQnkPzsF2hfNI8jv/IjbDvpSFxR2oZ7Mero5OueyHfd\nLaB8taiY1/AzdUZcXbJISeWQ/dvwxIZxbK7qefHVWToHnBver6EqaeW1Vs7Z8vm8MxJqeHwhppVv\n6ujMzAyGhoZQqVRQKpUyDj+KIty9fTsOPeccAMDPxVBZVKYlA9Zo2ZV20sPiXzpEOnuOh2d/r9RW\nNFua4CIxutIWRZEzSk8+MjVI9S4QBkD5UZcvm2AOloO08HtJsZc+s6iMPwpNrVVmPoTP1OVMX9Vr\nmACv/9ZPceIvzsjQoopv655sU2+k9+h3SZKgr68P69evR6lUcu+Lb7fbDhnR0GsoY0MnreYmHzjh\nigo5R51OB/V6HdX5ecTTJq4PADSA0elrnFLFcexe6TM4OJgpkqPB5IZP8oL72Or1eiav1+l0MP+k\n92Lh479F8T1HAQDWnn0dTjzjAjy1c6sbAxdIaEiSJHHIj0pqZaCXzHIMLEexK1saevtSChZB+VbQ\ngKXXeHHe1YGoIVTdsSGY6oFFer4UgQ3rFKGR1rm5OezduxfVanXZ8+669FIEz30uAgDnA7hgcU+b\nbi7XsJ6nKPA5NqdMHvF6ntChq88cm67er9RWPSVAB6wbL5nPaLVaGH7Gj1HOAXEC/GUimxxXgkis\nzUNoPsmXK+jlfVRZrcVWhuhv61WbaZSCAMCWexOEzeoy4VfDaA0D6bCxNMfNv4nmtmzZgm63i3vv\nvRfT09PLlnC5Z80Kk1bHaniqvNCQjmNkzqbZbCJfKOAPH//w0qyHQJCCNpT7yigWi+h0OigUCu7l\nhZx7Jk+ZeNdEOPNQQRC4sM/ODwBUX/oVFD52PPKHpgQc/u3LcCLuwECYuJVbzTMxtKMC2PDHGhjy\nmEZa5coiCl8IR9Rk0b0aDvanm2YZuqhck3ek02dEbUhEGvid0mHLcXit9qmvT+KLA+hcut0u7rzk\nEiQnnIAAwPdzOXxtdDSzuqaIzBokX06IdLBejjznW1dIU6lUcnV1/1eOLmHnNEKaWK3X61hYWMBz\nn34fAOAjHzwQcbJEsCqIhc66gXTZb2RhrRokToIK+/Kc1PIzwtViU3nDMMSXX/ksTIwv9dUNl7ZH\nqFFS5KTHP6gH4z2KzEgPQ6j5+Xns3r0bzWbTIQ3tg8/UmhANEagQVkCVx5w3RbS8dyo3jGs/dgrC\nA/OY/senIEm3QqFW3JB5RRH5TaNgQ0mGAp1OJ7PSpyiJSqMCPff0U1E/7xoUPvMiAMC2My/ByWd8\nEy9rXuf6tCu5amx8aEn5w35Jo08OyGtVdrtKvJpxUaSqyXo2fSafYeVTn+0LkdRAWIRkQ1qOgwai\n2+1ibm7O1S795Xe/cwjpv/J5fI3nIpk8L+Ukl8tlXm1GHqsB1tMvmCrgAgkbQzqieOYsV2qrHoer\niqfJu1arhfCJ5+DVn/ky8mGKkuq/PzBzv4XLlqFq5VWpbaytEN5OnAogJ0STbTZWVgOWJAny3fUo\n1dJ+/nBUH4JC2cXqSrfS7luGJh2+FQbeW6lUMDExgb179yIIApdHYdM3zFrlUSXSI0mApYPndEWS\nAkU6m80m6vU6AKDavx6XnfJlrPn279J+/+nRWCgduCx/R/ro+YIgcM9NkmRZqMS8WK88DWkEgPpT\nP4Dy518C9Kdj2PL9W/CS5nUOlWnylH3aPJ7+KM/4t5U1/aEB0flVo6GLGBqy2DBOj+pR+tQg+oyb\nT8aVXv6tz1C5ZZhlw01F8u12G3Nzc/jzb3+L4gtf6AzS18fGliExa0R9vCViJUrW/XZKu/KwVCq5\nSnOmEVZr+7T6pmEBUVKjWcOzj7kLQQC0usAnPrce+TAPt6CVLE/M6f+2CMuufOlk2RyCMstOvsJ3\n67GtUPLvs173cADA31zTQBItJXO1utn22+tv/g9kzybiWyi43K5wm8rXbDYzeTHS6ENNvJYGSnNl\nmS0Ei0KmRhpIV02waNcKe/YiSRJXAEvaudGXKI8Cp2+apQByzDYJrzJk56z1tA8huOomFM56NwBg\ny/k342WtP6BQKLhwjiEd+aZ1VDbEViSt25t8Ds+HOFZD2+xDX5CqZTGKlmx+yoZplgbVBTZLg+96\na0jVecVxjHsuuwzrX/e6jEFSNM1rdfVaUy3KO0VJmgRXmaVuc9dBf38/wjBEq9VyKYDV2j4VT+qq\nG5WnXk/hRTsC3v/MJ2PvTw/OJrFMfGoZr0ajV79sFiH5fsg49YD2mVY4+f/JZ/wJCYCfPG8DkmD5\njnVf/76mnpotjtOjRQ444ACHMq2gE3naUBdYUnLliy2FIK2cK0Jre4B7sVjMLP/e9N63pl+ctxMP\n+fF7HB1cSSNSViWn0FmDTUOknroXimGjgtce/vfIfftdAIDN59+EVyU3YePGjRgdHUUYhplck130\nUKRq//alDxSh2/o7pd2GShoaKp90B4PWMPUybPzch+D1bytjNiRSmbAGkM+fv/VWPPbUU51BOmPN\nGi/CpxOmMVHAoGhQS0yss6be0VgVi0WXnmg0Gq52qjA8gtXaqqcEWIPEOpr21qsXCbJbQwLA5ITY\ndBA2C99L0fU+oLehUJoZ36pRtP3o7/xi1LRr9FDvM7V/VTb7XP1c6RwaGkIul3Poht5FBUQVQvtU\nA8o8jiaZNaltK7CJmhQZcpUsl8thcvTBuPnUt+Lwj3wZOHcHDsf78Kfn/Lsbky7T67gUwZFGG1oo\nTzgeJkwpT0pr/JjXou/cAhqv+BTWn3M9/rGSHpPS+NsN+OK641CtVjN02Dnn82lI9XNFQqRLeaQK\nrtfxebYvlsNoWMpiUtZv8R7yy+dIVb6sXFqaVc6Ur/o9ZYgO4omf/rQzSGeuXYtYHLZFjcoTXqOh\nKq9VZ2cXqRRp9fX1IQgCV3PXarWASj9q//5trNZWREr07BQil4DcciVe/carAADfunCNyXEs3zxo\nvX8vlOO+w3KDoHDT51VUoTXc67Xqoc8/71XjCAC8/qztQJx93bBtFoHZcaoR4SRXKhXce++9Lp62\nuQgiG4swyHcKPj9j8lCv4aqGhlOa42Di0tb/TI0dhtv+7R3pQM69D1t2/cwdgUGYrzkrHafWLSny\npOwoYtEwh9+FYbqPb3BwEAMDA+g75m0YveAjaR/19Kd80W68a8fPXG6LqMnmdjSvQ4VR+fLNp13N\n8zk7DYs03zZ62Z048YvfxvO+eBaOPv1CNOeqLjGvK7XWSaj8+EJJX2Shss6/dUGB9yrqq953H0pI\ntfFb69dnnLTKp4ZunDdd0VQkxVVZlQMtWwHgUFJfX/qC13q9jlarhXw+j9rHvgbkVz/CbdUzummM\nXIwf7sLfn/w7AMB/fG8U95z9aAf7bFOh0MlQQbEJPF8j8+wkWFjM39bqA8sTiXr/03+1FwmAHQeF\nyBWWzqTRcJDX0yj4BEf74kSWSiW0Wi3Mzc1lVsM0RKQwcVe9LvezHxUW9sn4ntersbPKFcexeyWP\nFr2FYYgHXf3fjvbp8ccjjmOUSiUH4zV8Iz9otNg3r9Wle8qOfkZDNDY25rbU6Ftf8096IzbcczO2\n/OkX2HTZ14EAKGyfwZujG9NK8XrdJcI1Z6fhiC/fpAlw/UxDD58MqvJyLgp/mcRzr/4RxuIZjCUz\nOKhzL1721TPQmJ13uS+tX1Jnqf0rArZIycqt/q+OWBEZ/2+323jyxz4GALgY2e00HKMvTFX+2D2m\narySZKnIV+nh3Pb392dWY5O+CmZPejvQP7BMV3xtn14coCUAncG7AAC7F4C7v/0oAEvKkxoBMne5\nwemVA7DXaUmARVFURDUsGufzOlWCDArzwPh1D6RU//dTX5spWqRAaFJa63d0Uq3hpZAUCgVMTEy4\neiRb66RJ7DAMXc6JE6yoR42VrW5XZKQCDyzVKlG4dEtHPp/HzKGPdHQP1+5wYQjzA1EUuaVc3WYS\nhmHmeBKuwrGgVrfEFItFjI2NYXx8HCMjI66eyY6v2+2iWxxDNP4YFB7xUoz969MBAAf8eRf6+voy\niWa7D01liIZQeWOdkYYw+r8v5OOch2GIkXumAAA7sRGfe9gLsRPjKKOFl331DIeYdK4tktZ+bf/q\nNNVp+5CefUYQBGjt3Yv8Rz6CMtItJB9fty7zPec1CJZOJ9X3tqlu8DPOuzomLjqwfyKpcrnsUFK3\n20VU7sP8J76B5MgnAQCKD9yL1dqqiW52zo2hbaSFLXGSRSVkqLDMC48VJdnmBEJCLs3BAMgcYmWT\nhVQSDeVUMOhFfXA6ARB6cjq8Rgv4enlfTrTmKFihzRyErmxoYpF5MOYneGCbVS4qHA9gi+PYKTjR\nKunlKQI0kGEYolqtAoCrJG80GihOP+D4vRAMuK0pSZIuzxPxkX+kj6/TIS8bjQbm5uZQrVbdnr3+\n/n6sXbsWa9euxeDgYCbfB8AlT20IRsNTfsH7Uzm4q4N3da51K3M2VOJckGfqKHR1zrcCp47POkxV\nfCpeGOQWedWH+zfO4VNHPxT3YSPKaOGFp5+B1qIDIn2KtnqheKs/1rn4QjyVs3w+D3Q6eMK//Ase\nPzmJBMAfgAwvteaIMqjpAuqIAhGOmbTbQlH2XSwW0d/f73Kn9XodzU4Xkx/5CpDPo3DfnTjm1ivx\nhaHViydXDPC43aHdbqOeux/Fx16Clzw/3V18+R/7MpXWClMBIDFeSIVA4aw2FY70g/QzfVuDXuuD\nv7Te6jmZmLMKzrAIWEzPJzGA7I5xFQIVUP6vYajmc3jP3Nwc6vV6Bi0qH3S/GksHOA4VRKU5SRJU\nKhWHQCg4QLqRVj1uo9Fw9UNEtK1WC/39/Y7GQmfp5Z250iCCIECtVsPo6KhDJwzpmPzudrsOJVWr\nVSf8PHqVJx3Y/AN5FkWRGzt5SIPCZGqn00Fpw6Ox/vJzsOdJr8LwT+7Hu5/RxidxhDvLnOUKVA4d\nu6JS0qAoiXxXFKAOUFfc1EgtyXXiTnD48KM34ozrd6EfdXRqNXQWEbU6UJV5lRE2vcbOvdKkMqkJ\n5oVbbkEBQBPA54pF/LK/H4nkhOgIdTlfaaI+aNpC84u0Bero2XexWHRheL1ex3yrjemHPRooFIBm\nA6cFsxjYOOaS7Su1FZFSu91GI9iN+pZf4Pn/9B944fNvQQjglzfnceNnj3EJV8L9DPqRAfs8g4XJ\ndiUkfcbSBkUyQfMcnDC7MVBXEBQxWbhLgdy9NUVKL/r5NxBF/u0Mmg9QhKV92rxAvV53hYcK5zVk\n41aKXC6HgYEBd62unukeoyiKnOdi3kcFyCoQDRKTjzRoRFHlchk3H/0WhIekq3Kbrz3b3dftpm9j\nJW/pATmOhYUF7Ny5010zPj6Obdu2YdOmTRgdHXUGjDlJpgM0VFUEQ55oPVK73Ub+Ic/G+PazAADr\nLtqL9+AGF1JqKGodDhWJz/IZFosWeoVt+hPmllaaW60WarUa5qvz2IUNAIDnnn42mvI2WqVHFyy0\nZod8sM5NmyI/ygUdW7taxRO+/GUkAC4H8Mv+/swiiTW2Oj41msDSnj4mtTWPpBFJpVLB2rVrMTY2\n5hB/t9tFDQHufO+/Y/4lbwAADO682znBv3qbSXX8Ehzzuo/hWc//NQDg7lngO78exCWnPDUDfclc\n7VDhqk8YfAKgyq+TpXBbV15sLkFzKgyJbBzO57Dlcjn81zNehdkRYOP9CV7082+4wk+bzNblVgvD\nSSevYUKWgqbQnQJCZaG31/oXFr+Rv5pg1vi/2Wy6PUrkiRpA8iKXy6Ferzt6GfcTccQjS3vWGB62\n2233AkPubYvjdF/V7t27MT8/j2KxiPXr12PTpk0YHx933lINsXp6CibHQ5rVGFteNJtNlA4/Eesu\nPh0AsPF3e7FhwwZntLgiR+fFnMdKuUQbwvEzG9prIt0pLu/FkiNot9t432Fj6CDACOYQ7Zxyx8fQ\nONijYSk3Kke6qqY02TGo8YobDTzmne9EGcDNAD4xnu6b4g4HGi7dg2bTJxo+0+iUy2UXtmt+UBES\nP3dyjxC3v/1fgXwBqM5hzZ9vwMdGl87q/6uN0hFPuwAAsLcOnHPRGnzj1cfjli8+3rsSpkemAmn4\npkuLmqnX+8gQy/z0IUswVyGl5kis8bBCBfiNHbC0ghREeXzjha9Au5AaplK0a2kcxniqV9HcCH/H\ncZzZ6a9CZ3NgDC15kBpp5P+qaAxpNF9Wq9Wc8WUOioiDQqCraBrqqfcsT98J/H4eADA3vg1RFLkj\nSJiH4DaBqakpTE1NIYoiDA8PY/PmzVi7dq2r3KVH1SOROXb13CoDapgYfqtTIc9Kj3hhelMHeHXw\nJ3ecipYbaP2Q/igiY79WLtR5aOPnNhSjDBG1VRsL2IXUIBx33k/QWFwO17TBsmgA2ZBOZcPqCumg\nceNP87zzUAawC8A/b96MwcFBbNq0CWNjY27ONapRXVL90FCaCzrWKPucay6XQ7lcxlyni5ve+CEg\nn0ffHTfhw7tvwikjAfLizClPK7UVc0oXfe85CDbcjskfHIWZmRkkSccxhQQv9z7+PBIHb5NqlvE+\nZGNXMSwC8nkeK4DW8KlSBkEA9N+LwmIRZYRy5tn6WxGRDQOCIHDJX2tAKQzsk/E9+cJn6+RxO4om\n8HWMRFgUIM0TcJw0cvxcN03y54ifnplOWw7Y8aCXom/xvCiu1BCJtFrpW3NHR0cxNjaGSqXiUBoA\nt/oJZBGdKgDfN+YL4bUeSmuDOI5cYQBrf/hRTL7go9h47g34xxc38fn65kylMflsDZomha0M2NBK\nUZQaBz4XlL0gu+uh3W7jA4euxdfvmMGmeA+ecfr3cMl7TkLHlFcwF6QyRRqtE7R/azhWKBSAOMaR\nV6U1g5ctOhFFy6o7ND6kV3VA9YWoKpfLoVarZdIgtnGu2vkCbjz5FCCfR/n2m/CueBr9Q0MZW7Ev\nKAlY7TylOw9D9adPxsLCQmZJ2S6FK2Nt84Vu1qDYyUDgN2TA8olTWnTflfU+vNf2zWed/N3LEAD4\n2fM2IiqsWYaq+CzNXyiEtnE3f2xSUe8BssVqPD+I5QM0RhQqWx9FQ0WaNPzQkgMKFFdRSScV44rn\nvScdYARsu+9CR3ej0cDCwgJmZmYwPT2NJEmwZs0abNy40RklImSGgRRgNbgq9Fzx48oO++K2BCbs\n7Soct5mUj3k71v4wLbA88II/Y+vWrWlOZdFoam0Un2FX3XT+VUZUbiw/jSAs/s7meaIoQiNp4T2P\nORJt5LA53o3By+52jkBTDorI1PioY+pFL+UIANqf+QwGAUwC+NrIiGwDqzt50qNJ2KgrurxPZ6Vy\nZcNg/lZ5bucL+IMapGgKw8PDmVMwOI90oCu1FY3S/Pw85ufnnSBbFKOM1Vh4ae6WH7TOz2nxbY4m\nw/xFT6RL3TZU07442Ww2VLKeV3+Ki6hy59hxjtnWiKmQqIElHyzM1UPXLCrolbfgM6moWrCm3tyh\nh0XhYaikSsXnAMiEgtY5POmHn0lvyAP3bXspAGDrT96PQ1/+Ihz2hhejsHAPBgYGsHHjRqxbt87l\no3wKTKSi25I0H6H5MM3R0VDS+Oi5SgzLmNgOn/AmJ7nvPvcsvGP0frdPjvdov1pa4ZvDXg7I/m+N\nmP6tSLkeN3BL+GAAwOHXXpPZG6eInferYeNndBjWELnwPY6x5Y1vxN/ecQcA4Me5HDqeHB4NEn80\nOrB5P6YBuHDVWEzWK2rV3FQQBKghwI3/uBSy/VNnwhkkddRE3VxdXqmtaJS4nG0Z5Au5rEGKkd3s\nl/lOhIMT4JtoPr+XAvP51ujxPouyfNCY19pmFUYhtkWJRAs2L6Re32fU2QilaYTK5bITDhoWGlvl\ntxpERawWnWiFMcekBvfaE9+SEtIFtu74IQ7+1QeB/7gJaAHJDPCo007F5s2bMTY2ljm2lzki5ivY\nJw0JlZHGgat9pVLJhTA0YHoqgObqNMRR5DT61Vc5mh/0gzvwjpH7sXXrVte/NUpWVlQe9H91Xuy3\nV8Jc5VMd08LCAs542Bi6CHFQ514M//Rm70H+PtSuc2jnmkYhDEOMnHIKNkURAgBTAM4bGlpmSOkY\nFW3zRw0S51DPxdL0gQ2n9f+/nPx+IJ9H6Y6b8a5oGkNDQ5lEOuctn8+nW4kWHdpKbUWjpG+1sKsG\nOonZ5F82Sa1M5z38beGyey4/W7xdcyC+VR2FvsDyV2TbflTowjBEkm+h1GCXyyvQdaw6FgCZpX16\nd+2LtVwaYumz+Xx9cZ/Sxs8YAupKhyZeudFW6dUEMI2A1grx2sf84muO3uEv/BfCr92anY+3Pwv9\n/f3umbxPkSH5T1StOQRFdxr+6lEfqni9Ess637kTP4X1u+/D2h9+EAAwfs71+IfSHdiyZQtKpZIz\nmorSLO9XQtzWYHAOrOKTVtLHo413NXbjskK64+HQ2252BtrKhzo5DeNUVngdr4mjCActFsG+Z+1a\nvHhsDG3jNO1ikMqGb8FFnSj55nOElAEiHywinzc09mBgYCBzJjifn8/n3avD/uozuplHstbRKrka\nJ2dJAn/tjjYriL5rbfyqv62X0+ttmKSJTiooleqVP/8ecglw9yEh2vnyMmNH48D+VBlZQKh5DLsh\nk/1aqN4LMaqgEAbbuJ5og3RoDZQKkW6b0bogNVqFe2cdv+OlhUcAQOETJ6Jx/KmOXkVd9Kj84WdK\nC+lhf+QTVxOZR9KiPs0LKr/tZ90oRPD4t2DNYo5p5BtX4+0j92PbtnQFUV+yaZ2YnV/lrZ0blVFN\n2lMWNQ2h4eN5GxPEAA5p343DvnFx5vgV6yRVNtQQ+2R45H3vQwXAPIDr4+z52qqnbPq9ypo+X8M2\n6r01MEpLN8zhlqc+HyimOaKRkRGXriD/FB2Xy2Xvyqav7dN730iIXYZXpeAEub1vyHbug6M64Iwg\nGMJ7IQE7oUEQZBK4vQwiFZXefe3ulOqfHPPaDOMtCqSn5/MHBtINhkzcki4Kndbd6OoLx8vriLQ0\ntqdBsrUtlUrFVUrzd6fTcZOu+RwNc5U/Or5Op4Pbjj/aO//5jz8HzWeemlmeD8PQnSDIfvSkSBva\ncozFYhHFYtFtiYnjpQPqLLL1hU58Ho2ihn3RY052pwsMff0KnBTchHK57EoziE507qxjUpqVFpVf\nri760gRWltvtNnYmk3j3oU9EDOComd9j/feuzeS3tA99ji0lUb4kSYIHLSwAAN42Ooo4yC6CaD5T\nF1joONVZMaxSB6fGhHlNDX1p4O56+2mIHvtkIAgQzkxirK+U4Y06H92R8VcbJXpEH5y1/wPZo2At\nhCNj1NPYpNyyZwfIMAJY+RQAXXHRlSuLUNhPBu0BiHugGL2eiKNQKGDTpk0AkHkJJ42BCp49NZLj\noLKzLz0el2PkeEqlEiqVCoIgcAhD3/7BpXY9YZLGTg+C4/nNuhLWjULEj1zawZ172yOQ/Ne/oXrc\nh9yzFBGyEYVQVsgf5obiON2a0t/f78YWBIHbuqJbgjiHyncNu8h/K9T8PDnqZIxdmBqmNd+8Du/b\nOOEMiU0y+/KQXDUFsMxgqNPLLH4ES+9E5HP5HY31XZ0HcOboUwAAD7vnBicnnDtfOkOfpX0nSYLR\nd74TfQAaAHYGS4sduofSGnY1fFqawv+ZE+VLTqk/NMLlcjkTuhcKBWBgCADwhJsuw6ejPRndJ88p\nN7ofdF/aqkjJTpCFvezMFk9CDIF6IXpnei8qnsJLHx0U4KXHL18y5QRpElX794Vl2qeiPxVa3VzJ\nz7Zt24aBgQGn3IydtaiNSqHhnAqhboFgfwxnoihym1255UPfNUZjNDMz49AVUSu9EgWOSdZqtepq\nqPhKp6f98Ww8/MxfI7wx9b7tz78a953wacyO/20GjrNGifOlHpjjmZmZcRt4iYxIF/nL0wb6+voy\nR+OoMfLlRPR7u//KrTD97Zsx9P00x3TQhXfgrRumXUhCw6TJXZVxlQWb57HypnKnCk4l1Ar7RqOB\n64aqSACsjyew8bxrXKW6HYfVE+oT+TD83vfioc0mmgBevGYNIMnnXnkqPo/OylZmaz+UHR5xw20j\n9Xrd6V+5XMbI6GgazSQJXrppDH1S9U356uvrw8DAAMrl8rL5W62taJS0YM+GQmpY6CG1OCrG8lMC\n+VtXk+I4zkDPMAwzoR+VSxNkykw1NpooZt5BGW6NEADEw7sQCK9UIYDsm1kpmLlcDps3b0aSJO4V\nNtqPjkerla2Qq/LymBLNqVChCIN1FaVWq2Fubg6VSiVTec2cBpPOzWYz4/VI8+jMXXje78/G2E92\nAABKx48gPut9qD7qNY4W3cKhoSX5QmXghtxKpZJ5AaLNkRHJdTodN04mVDXfYJEMjaIeWaLHsfCe\nMAzR/9R3YOxTJwAAnnTLbhx00EHuGGA9VUANU6+wzRof/a3XqnO2qLzdbuP+7m68f/MTkQA47v5L\nMH7uVQ4xVSbvwvj9v8eGXddj464/Itdte3c/RFGEhy2GbSdt3YoGlr/klKtndIiKxCgfmlbgaq8a\nJpjB3WIAACAASURBVF1Y4bzo/svBoSFc/cLXp33Oz2aQN+esUqm4kE2dio2UerUVK7p9iW1Nxtnv\n0ra8WFGZq/eT2XqtNRpa+Efms3Ggmky1YR7/1r7Zz1PuPQ9Hnbv4Wuj80jMVDdBAaCX0hg0bMDY2\nhsnJSTQajYyyaqjBZ9mkKbC0L0n7pCcjWmL/mgQmygmCwF3DzZNET/xME9s0ZHEc4zU3nofKFXOO\nj/PffB+ig5+NUqmEwcWiyTAMUavVnIHVEJRGIo5jd6CXCjDRC5XLzk8cpy+7ZEhn80ZqkMhPInFF\nhHbOoyjdrFw+/g3A+36K5IEYLxmdwOl7Qhdaq/OwfSmK1rnyfaYyro5MwyUd682VHXj/5ifhkzsv\nx3H3/Q7bv9vGEQ+/F8O/2Jl53qMKwBXveiOi4gDy67YgKZYRRRGKH/wgAgAtANOL+xDJW63Oplwk\nSZLRGc6NzgfvobHmGEg/ETb3PVbG1uAPL30TsPEAoFHHZ+M9iLB0Znwcxy7nSZTM1ssB+NqqZ1Oy\nOE+VWTujoDCGXFp88ye1rBeycbO9R5Xc3qf0UBHVECkKU4ELggBPvfd8HHV57DDZlUcPuX40+arJ\nUCCFwY94xCMQhqE7N0ivV8XSozTs2OyYaVz1DG31VkEQuDyNeh+F74rOdBUQSPNerVYLb7jtAmeQ\ncgcGmP3o+9E64GkoiCEF4F4ewBIETSxrIRzhuTVG5IudM/7mc+nV+TnRRRRFmdeNEy3rfGjeRX8X\nDjga677+Wkz8w7fwyLOuxMtPOBRn7ihnkuQ23FHjZr9T5xAEQWYjlV7bC0WxdunmgXtxyuaj8cmd\nl+Lo+64A0tclIrdpMRReSIB54ImfWizRCIC9p38Y937kKzhyZgZdAK9YPPwfWL4/0IbXVj8AuLnj\neDUvqCg9c7JkEGLu1K9gdmxtSlejjs/U70F+Ue9pkAqF9LXvepyMNfj70lY0SopofJNGBmjH7l5k\nK6aVIGWctaBJkmROniSjbKJMlVPpogBTQC19QRAAQ3POICXp3OPJv51Hs/QD3Lb5BV5kyP8HBwex\nbds2zM/PuxUojknHqMKsiEvDXSqx5qOApY3CNEa6B46GjvTQmJGGdruNhYUF5/k0XPnH2y9E+fI5\nIACmv/s5YOPfpIZBDFiz2XQGkbQzDCQSYchJ2umJOX7mIqy8aGmD8gJYSjRrCKu85PX83HduFo1j\noVBA5aVfwNpuHZNv/h6e+us7cPbhj8mE0j4naBGRzSP5nCbHZ6/jb02wdzodvPytTRx4OHDf69J7\nB7/3FSSHPjvdEtLtovDeo9G6dDoVzDow/tbTMA4gBvCazZsx02pl1rWJgGjgbcjsG49GKVo8qWi9\n0+mgGceYO/WrwOAwB4pwegKfiPeiKCg3SRJnkFjCouBjX40R24pGSRO77ECbegLbkjj7imxe74PE\n+nxrUVWBtYJYPbjCeTZCWgqh9p9EgvQAnP7WI/D2L9+Ap/98Cre/uokkrDgajr3yW3joTakCdAvA\npaf9EwYGBrBz5063UmENr65EMOxiTG95oAZPkQHv5Twosuh2u5kjS3h9o9FwZzgBS2/SOHn7mcjd\nsPhm0gC478x/RWHN4RiQw+/UY/LALpYbMFSjQdJXNCv/iZjtihTnw6I8emMaNX7HvzX8VN7pmH1G\ng0eulJ5zCvDm7wFxWkczMzOTKb2wzaIKJy9mftUoWDoos/oThiHOOXUTnv602xDm9iBJgOSiX2Fo\n3aGpwZ+bc4odfPEqjC6e0ND65LNR/dIfAQA1AHOLck+50H5VbhTtc5WTOmMjCf6txZNMV8y9+GRn\nkII9O/Heiduwfnw8U71PfWQukc7K6r7l0Upt1dd224dbKO5LHrPZkInXadzN1ssL2VBEjRYZY3MB\neq8PPob1IXz/ZcOol4FPv+EYRNVHYX7RGbzpO9/BIff/AEmSYKx6hTNICYB8B3j8v30B3e7SeUPk\niTWONp7WEEAVWwWf/NR79HvWB+lZ5Po6ZBok3Wbx+kvEIBWAmfM+j3DTYxz/fAWG3PfEUgd9saAN\noZTXFHJd8NDn6lzqu+uApS0RdCT8jAZM51z5rLxnHy4Jnl/c0tABHjued0ZTt5+owbGoy8qmT6ls\nTkrHSYN0/sc34Pjjb0OYA7rdHO5s/BqloQMwOzuLWi19f2JfXx+Gh4cxNDTkqp9HT/sdht5+BFAE\ntn4fWLt2bdY4LkYCmgvS79UBWESv19j0gxt7JS0TGb/4f/CO+2/A2jVrMi8EoJHjXjg9JkdlX9u+\nGKYVkZKuHtnJ0b81ZNKJ1EmzBKnw2Um3xoWWW6GxnRxf/Gz70efdFT4bX/m7PHJNACHwzZcfhbf/\nxzXIR8DxP5vEjtc8gEfdfBsA4OJnlnDVAc/FKV+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ZW3JQ3B7CXCCV2ldQyHHoIgl55UOB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uLJhErUNQzfjqqrcs1mE337vHf99oST4ZoWHj/0lXANoKMAnHVjHsft/ZXPIwS0ElxbUgQJLx6P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\nmHa8STm75O6h2rdvH4Eaj1982iZ+uHAslmV5K6OYf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QnAxfdtYGhPkmLR1Ub6+/txHIeJ\niQmF8i7B8/m8F7HbsSPAWBt0jEI8YjFRdH1HUu95cnKSYrFIPp/3ajBZlkVnZyfxeJy5uTmmpqZo\naWnxom3VatU9/HJ0lOnpaS9sXygUCAYCPB4fJfbXEfjr/X7iRuDRYh+BiQlvmQmHgwSDfu1Wr36R\nSMTHRNpvYts2jx18PnYXvDryAO9v+Q5fO3SVz+TTjKdTITQzm+aX9slo5q1W3YxtKYcbi8V8WqnU\n+i6VSp5Z+8V738zHz7iOi/pu5vanX+e7tzTTJ6VNzfp3dSAoFis+4TM1eJGFRj4ex6ltUUrEsWZn\ncJJprEhTXbOJRMCysGYmaYs3eXSRhV6DpLYuNEA1SsPRfljT72rKnp6zarXKISKMpl1/43vnh2lq\nzXjVJPT4tAKhQdSsEmBaRY00tecCUEcEJUHCRnWJhYmlxKdmCLczDrYCIlONkwkWgmq1T18vzXHc\nZDE5jqenp4dEIkEul2NqasoDK7uyle+//XQqlQoDAwNMjkyysDBLqVQiHo+TTCaJN7tO0ZkgjB9o\nJ5ebIBaLUalUvNK3MzMzVL1MX3ccY2NjrFu3jmQy6Tpta9S7YEmBG+xOQqEQc3Nz3oGTra2tDAwM\nUCgUvD1oUrpkcHCQkZERdu3aRU9PD9FolN27dzMyMuL5YkQAigsL7G0tEtnTeJ7++r4Xk81mCTiz\nWLXKA0Pj8zhO2POPSXhemErOnBO6eqHuGrNVKhWeHj0deh4gWjOMRYvQ8ySaoK6UqP0GetHSjKvN\nd80Dug8iaNofISF7e3czs6eFSARK5Jv+SLX6Jgq1wmqofDW5p/ZTSb8dx2Gw6ym3nwXLlyNUqdRP\njpHES/nccWrlWkJ18dGLt7RgMEixBgRajzEjzZoGch+tUWqtUzdtQsuCawKlBn95tvi45gNRpPD/\nloxfgzYVBk1PvYDJc+Q7MzBiGRhwJF+XR7cjfRkIBDx1Wkdq9MPlunA47PlO6vuP/PaoGQnRhDfr\n2GjGlpB6S0uL56SdmppieHjYS0jTu9ylxrKYBCJM8XjcTWAs1iNW09PTDA8P09bWxoYNGwgGg+Ry\nOR+xwRXcqakpCoUCXV1dxGIxPnniOq798VO8+88L3LJ+lP1TMY+B4/E4LS0tHDhwwHOq53I5Hnjg\nAWZnZz3gisfjnu8jnU4zPDzsjVlOz7h3RZHIU0AM7rjuO1jRjCcAkWiMKOO8bOxiAvMu4+Vt+P22\nILbtX93MjbzyuWYUmZtAIEA4uh+AKotr5ehkST3HjuN4DnnRdEyGNYXLFFABRZ33FIvFfFp0uVzm\naw+/kg+ffCMv772W3w7HSebfTLz8IISgWFl8srMGXICvnPsbnNQAYJH6YHER/2mQsSzL4+9isehW\nH1UAUCgUyGQybgSuKHxb17KKq2tnmFcdj27ilzGjXOYCr2ljjkfTUhYyzbt6brz9gXbtBByFD5K8\nq5UG0ycki5fZX9PNI9+ZVtBzbUcEJckelgH7flhjcv1QScpDdUBHFqSzAkJ6BTWbJrYIj+O4Bfvl\n97K1Q4gphJe+OY7jO5deACwmGZG4yXK5XI6enh7Wr1/P8PAwc3Nz7mmqAowOHngUCgWWL19OZ2cn\nA3tm2LEG1u2EvmiJvVNVYrEY8Xh8kU1dKBQYHx/3ysXatk1PTw9btmyhUChw++23MzU15Wkutm17\nGmpfrdzJ4L+9gEi62y3JUi57Jtjpz7wBqyYfeRva3t2E49TVdjEBtekhDm9ZHc1V1XLyfLD1JwD8\n98LZPuYTQNJzI5qFOYfmSmnyhQeukYgHPLKaDw8Ps23bNvL5PO3t7W6WfjTqCVDhqT4+Z72Oq076\nCS/t+Ar3zBzkjNQOKg7sfvqVRA0zR7S6YrHI97b8lWJ2J2DR8aZeysNzPjNXBFBK2Yh5qH1BC8Kj\nVoH5YsHn4xEal2wbe0kv5ddcBo7D+QeeJNDduijVwFysNTAczizTLg2htfmZ2QSQzHvpig66D9qa\n0Xv49LO0hmua69pkM0H0cO2Ijm6NltopqdFSHiYgEYvFPFAK2AFfATEhZCOb1SS0ibLVatXTMLTz\nUv9G/l5YWCCXy3krrJyt1nz805z/kp/y4jW/9e49Pj5OMBikt7eXYrHIgQMHKBQKJJNJwiE3euVQ\njxJKDe+Ojg43UmNEe8Dd7d0czfPByja+sLmIU63vaZuacr3vra2tdHV1kU6nPYesOO9ld7kXFq/R\nIjxUB2SpgxQNLniA9NqfJlnyviS2vTh3JBSqH+Ej9ZPkJWCimT8ccqNI40SZHj7ep+nKnAtzitZl\n+mv0dXqeNcMKCEg5XllYBJAzmYz3AsjlcgwPDzM1NeUeOvlULzvyrQCsbbodgJ+Pnk17oNe3gmv+\ntW2b0bZ9YEHLPcdgD5V8/YS6/0mbbebG2OiIA8UU2FU+esY13nVaLsrlMqVjTgAgOHSA57elfH4j\nUyvRQGSaupp+2lTT4GEGFUyg0LI2GpJ8ucXHWkn/9f3N8Wse0PTT18l9/s80JQ0gekB6RdCqnBzG\nJ5VYLLt+um0jJ51mXK2amwNwHMcHRBqx9UvAUhyHkUiE9lOf4uA/WsmsGOa69p3unota+8Gse57b\n6tWrWbp0KVNTU75z7TXgyNlvhUKB0dFRuru72bNnD7j5jCQDZQ+wwpT4294B7NqicFZnma35FOWK\n6yyXQyzj8TgDAwNs376dYrFIIpHwUgokbSEWi3HLshKv2vcMbdfs4ISptzK49TxGV78KpzTN6Xsu\nAGDOgt8/7PhqX5tmlT44QG9xEFPRp71KRIXFCYmNNF/5TBJAtR9EC4j2J8r/opHIqS5y7FI6nWbt\n2rVUq1XP2T0/P++VMalUKoTXP0NHKIdlQWd4mooDhYlziTr14EujV8VecLeVzC0QAS8DXbRp3Tfh\nJ+3YtW2bYCBI8jzI3QlED9TpUJv3UChMvrmN4qv+BYCth54h0Nvm8bwJAnrONLDLd+bfeiE2PzPn\nR7tLAMatCLd1LwUHNhTrcmreT/pq9lfLq37W4QDINKWP1I4KSlBXwxvd2HREBoPB+v4jy2ooIJrg\nZsjxcIN6LkgrzC33PPM19/C+wjyls/fyg0wUpmB/BG6bTfHIrhC3/CJKW1uSzZs3k8/n2bdvn2fn\nF4tFIkG/eVooFFhYWGBwcJCenh6WLFnCHzb0sW7Hfr78cJ47WoPkKmGWxOawqzAbhWIUeobgnp4Z\nThmvl9SVfh48eJDh4WFP+wK3KNgFzjNkrQq3tR7PX2JxOi+KctrPHyd10xCpm75L6rOHWNl6K5YD\nBRu635ckELA9Z77jON75dQLmcqRPKBTyMsyhLiBa3T4SY8kYtAak51T+1wKmGV7exb8j10lyoa5M\nIdEgzUcCrs+74FbOa31S9Q9uOPQa0gtdzFfmfePR0a2frd+Ok3BTKcI3z/u0CP0cASVZkHRSpWiI\nlrJGTFAKh8OUN5/kjuPZHby4NeFb3E2LQ5s9Ws7M//W1+n/TyWxGw6SP1WqVJ5rcY9kpWXxuyRzB\nYPOi+2nzXy/+pjVjasC6DybvPJd21G0m2jmtQ5MmeOgJ9NQ2y1qkypm2smnCNdKUYPERS1rdbTTg\nUy+8i/cV5rFwgzFvnXJzjrZVInzr2mXc8esE4XCY9evdwxZ37NjB4OCgd89k2xBXRF2H932TnT7H\n8NDQEOVymWw2y0zqtWw71sJ24BXLakxZ6+d0C/zh6mtwgOUH6hPT0uJukRkYGGBqaopEIkFHRwet\nra309PTw/f13ccntz3DebXv4ys0/JhoOcWN8C396xTHe+Pr+7gJS3oauf0sD/pC+1hqF5tqPoRlM\n6KvfTQZpZGJoE1rnJ2mBkDmVe8j/8jud7xKNRr1jvc0SvYFwEKcvR7Q7SEtLC1tf9kfOa32SKhZ/\nm9vCnoWOWj8Svr6ZwlOtVnl6hZtSkf6fNYR2F33jMumnz2CTo78lOdakl1lLLBCwsWrAH6qWfdtQ\ntAw1AnUNUGZrZNppWjeSJVOGyzVxsR1rkbPdBGW9aMk10o70rEbtuSgXz6l0iV49pXOawWWwXhVD\nFX0zVVSor5wm0fXq2khdBRYRxxSScrlMMpnkLXk34e+cmxN846JZVteivR7oJJP09fXR3t7Ozp07\n2bdvH47j1M7igtec+SxU4dbmAAd//wIq4QLptFuxcWxsjJGREc/hOt0SwqFIjxWgN1WhN1QTSAdm\np/NULbcShKzwsVjMy2Nqbc5w8vIsoWCQRMLhir//lvhTRQgAFbBnoHn6ANN2hp5ZN97vbILICwEL\n+q/KEg7Xt5FUq1XvIEuoV+3UKrxca9Jff1YsNmEBWQpkOx5kbOjERSupqRmJmQP+mtLmM0yzRATC\nzOD2kvJs+JfzP01r0F1Yvv3sRZzb+ncsC7bN/J54oJ189VU1jvMveo1eXttfdBMxVea3FkbtJqhU\n6uWKZfHN5/NYVX+ypgaeGStA+aWvBWDp7Dh2umWRr0f4Xgv34XxJuplga86tvk6ajH/WCnLPkm4A\n2suLtWTNC6aWquewkXl5OC0bnrtv6ajJkzIYUbcbdUibJL6d0liLhEBPfCOnWaMVQAORDNwcpNxb\nduPL1SMDCQ4586yu6dQWFs3NzaRSKZYtW8aBAwc4dOiQ77jmQCDAk8NxaMtz9mSFtWf+kP4CXJk7\nhdKzKSqVCmNjY3R2uhrU3uVZuOsQb/tbicuY9Pr5bL9bF0laOp32trLMz8/TGo9y2z/+hpVfPCsj\n/3Ya7Z+7D4CBuSqtqQKr7jwIQGiDe9mkA8FgyIt6ai3W9AXqYIMAiPb3CT0DgQCWbXH58u/X6AUv\nCz/I9+2TfVqEvpd+juk/kvkywcn0ccg9TPOpYlV40zmfJBsssOAEiFgVX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Jpt6XvbIGRj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\nvteb+tP7unWxOX4+i4TYlmXhORfF8b5N48ih7oUXHLgc1YlepBf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       "text": [
        "<matplotlib.figure.Figure at 0x111bce910>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a id=\"statsmodels\"></a>\n",
      "<hr>\n",
      "\n",
      "## statsmodels\n",
      "####statistics & econometrics package with useful tools for parameter estimation & statistical testing\n",
      "\n",
      "####Features include:\n",
      "- linear regression models\n",
      "- GLMs\n",
      "- time series modeling\n",
      "- integration with `pandas`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load https://gist.github.com/glamp/6536064/raw/57146ac3406f7541d944264ed9aa85dad6e085a1/statsmodels_example.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import statsmodels.api as sm\n",
      "import pylab as pl\n",
      "import numpy as np\n",
      " \n",
      "# read the data in\n",
      "df = pd.read_csv(\"http://www.ats.ucla.edu/stat/data/binary.csv\")\n",
      " \n",
      "# rename the 'rank' column because there is also a DataFrame method called 'rank'\n",
      "df.columns = [\"admit\", \"gre\", \"gpa\", \"prestige\"]\n",
      "dummy_ranks = pd.get_dummies(df['prestige'], prefix='prestige')\n",
      "dummy_ranks.head()\n",
      "\n",
      "# create a clean data frame for the regression\n",
      "cols_to_keep = ['admit', 'gre', 'gpa']\n",
      "data = df[cols_to_keep].join(dummy_ranks.ix[:, 'prestige_2':])\n",
      "data.head()\n",
      "\n",
      "# manually add the intercept\n",
      "data['intercept'] = 1.0\n",
      "\n",
      "train_cols = data.columns[1:]\n",
      "# Index([gre, gpa, prestige_2, prestige_3, prestige_4], dtype=object)\n",
      " \n",
      "logit = sm.Logit(data['admit'], data[train_cols])\n",
      " \n",
      "# fit the model\n",
      "result = logit.fit()\n",
      "\n",
      "print result.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.\n",
        "         Current function value: 229.258746\n",
        "         Iterations 6\n",
        "                           Logit Regression Results                           \n",
        "==============================================================================\n",
        "Dep. Variable:                  admit   No. Observations:                  400\n",
        "Model:                          Logit   Df Residuals:                      394\n",
        "Method:                           MLE   Df Model:                            5\n",
        "Date:                Thu, 12 Sep 2013   Pseudo R-squ.:                 0.08292\n",
        "Time:                        10:57:20   Log-Likelihood:                -229.26\n",
        "converged:                       True   LL-Null:                       -249.99\n",
        "                                        LLR p-value:                 7.578e-08\n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "gre            0.0023      0.001      2.070      0.038         0.000     0.004\n",
        "gpa            0.8040      0.332      2.423      0.015         0.154     1.454\n",
        "prestige_2    -0.6754      0.316     -2.134      0.033        -1.296    -0.055\n",
        "prestige_3    -1.3402      0.345     -3.881      0.000        -2.017    -0.663\n",
        "prestige_4    -1.5515      0.418     -3.713      0.000        -2.370    -0.733\n",
        "intercept     -3.9900      1.140     -3.500      0.000        -6.224    -1.756\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}